Comparative study of WaveNet-IIR PID controllers applied to a 2 GDL helicopter
In intelligent control applications, one problem is determining the number of layers and neurons in each layer. The problem becomes even more complex when the neural network includes functions like WaveNets, where translations and dilations are additional parameters. This article presents a comparat...
| Autores: | , , , , , |
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| Tipo de recurso: | artículo |
| Estado: | Versión publicada |
| Fecha de publicación: | 2022 |
| País: | México |
| Institución: | UNIVERSIDAD AUTÓNOMA DEL ESTADO DE HIDALGO |
| Repositorio: | PÄDI Boletín Científico de Ciencias Básicas e Ingeniería del ICBI |
| Idioma: | español |
| OAI Identifier: | oai:repository.uaeh.edu.mx:article/10067 |
| Acceso en línea: | https://repository.uaeh.edu.mx/revistas/index.php/icbi/article/view/10067 |
| Access Level: | acceso abierto |
| Palabra clave: | Intelligent control Identificacion for control Linear adaptive control Adaptive control by neural networks Controladores inteligentes Identificación para control Control adaptativo lineal Control adaptativo por redes neuronales |
| Sumario: | In intelligent control applications, one problem is determining the number of layers and neurons in each layer. The problem becomes even more complex when the neural network includes functions like WaveNets, where translations and dilations are additional parameters. This article presents a comparative study to determine the type of wavelet and number of neurons that best perform to approximate the Quanser helicopter dynamics with two degrees of freedom (DOF). The identification is based on a radial-based neural network whose activation functions are wavelets together, a pair of infinite impulse response (IIR) filters to ``prune'' some neurons. Additionally, a PID-WaveNet-IIR is presented, composed of a set of discrete PID controllers with self-tuning gains. Through numerical simulations using LabVIEW, the performance of the closed-loop system is presented under different operating conditions, types of family wavelets, where the minimum values of tracking errors are given previously, the number of neurons in the network, and the number of IIR filter lead and lag coefficients. |
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